SLURM and tailoring walltime for different jobs

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Maria
Maria el 20 de Dic. de 2021
Hi,
so finally, I have access to a big cluster that uses SLURM as scheduler for Matlab. So far so good.
Now, I would need to understand if I am planning the execution of my program properly. I have a Main file, with several batch jobs. At the moment, it looks something like this:
c = parcluster('Cluster name');
c.AdditionalProperties.AccountName = 'my account name';
c.AdditionalProperties.WallTime = '00:30:00';
N_big = 32;%c.NumWorkers;
N_small = 32;
LAMBDA_SCALING_FACTOR = 5e12;%100e9;
job_geometry_setup = batch(c,@rectangular_loop_geometria_setup,2,{},'Pool',N_small-1,'CurrentFolder','.','AutoAddClientPath',false);
wait(job_geometry_setup)
result_geometry_setup = fetchOutputs(job_geometry_setup);
Regioni = result_geometry_setup{1};
induttanze = result_geometry_setup{2};
clear result_geometry_setup
delete(job_geometry_setup)
job_mesh_sup = batch(c,@genera_mesh_sup_new,7,{Regioni, induttanze,time,LAMBDA_SCALING_FACTOR},'Pool',N_small-1,'CurrentFolder','.','AutoAddClientPath',false,'AttachedFiles',{'discr_psp_nono_3D_vol_sup_save_noglobal_mex.mexa64' 'genera_nodi_interni_rev.p' 'interpolating_vectors_rev.p'});
wait(job_mesh_sup)
result_mesh_sup = fetchOutputs(job_mesh_sup);
Regioni = result_mesh_sup{1};
induttanze = result_mesh_sup{2};
A_th = result_mesh_sup{3};
lambda = result_mesh_sup{4};
nodi = result_mesh_sup{5};
A = result_mesh_sup{6};
A_obj = result_mesh_sup{7};
clear result_mesh_sup
delete(job_mesh_sup)
So, I have a couple of questions:
  • in the way I am using now the batch, I continuously need to fetch the output from a batch job to the client, just to feed them to another batch job, as in the example above. I would not need to save all the variables. Is there a way to not fetch the output in the client, and to use what is directly available in the shared memory?
  • each job can have a different walltime. Shall I just redefine c.AdditionalProperties.WallTime = '00:30:00'; before each job to a value that I find reasonable?
Best

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